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<!DOCTYPE html>
<html lang="uk">
<head>
<meta charset="UTF-8">
<meta name="viewport" content="width=device-width, initial-scale=1.0">
<title>NLM Denoising Simulator v7 (Pro)</title>
<script>
window.MathJax = {
tex: { inlineMath: [['$', '$'], ['\\(', '\\)']] },
svg: { fontCache: 'global' }
};
</script>
<script id="MathJax-script" async src="https://cdn.jsdelivr.net/npm/mathjax@3/es5/tex-mml-chtml.js"></script>
<style>
:root {
--bg: #0f172a;
--panel: #1e293b;
--text: #e2e8f0;
--accent: #38bdf8;
--border: #334155;
--highlight: #facc15;
--success: #10b981;
--danger: #f43f5e;
--conv: #f472b6;
}
body {
font-family: 'Segoe UI', system-ui, sans-serif;
background: var(--bg);
color: var(--text);
margin: 0;
padding: 20px;
display: flex;
flex-direction: column;
align-items: center;
min-height: 100vh;
}
.container {
display: grid;
grid-template-columns: 340px 1fr;
gap: 20px;
width: 100%;
max-width: 1500px;
}
/* Theory Spoiler */
.theory-spoiler {
width: 100%;
max-width: 1500px;
margin-bottom: 20px;
border: 1px solid var(--border);
border-radius: 8px;
background: var(--panel);
overflow: hidden;
}
.theory-spoiler summary {
padding: 15px;
background: #28364a;
cursor: pointer;
font-weight: 600;
color: var(--accent);
list-style: none; /* Hide default triangle */
display: flex;
justify-content: space-between;
align-items: center;
}
.theory-spoiler summary::after { content: "▼"; font-size: 0.8em; transition: transform 0.2s; }
.theory-spoiler[open] summary::after { transform: rotate(180deg); }
.theory-content {
padding: 20px;
font-size: 0.95em;
line-height: 1.6;
color: #cbd5e1;
border-top: 1px solid var(--border);
}
.theory-grid {
display: grid;
grid-template-columns: 1fr 1fr;
gap: 20px;
}
h4 { color: var(--highlight); margin: 0 0 10px 0; }
ul { padding-left: 20px; margin: 0; }
li { margin-bottom: 5px; }
/* Controls Panel */
.panel {
background: var(--panel);
border: 1px solid var(--border);
border-radius: 12px;
padding: 20px;
}
.control-group { margin-bottom: 20px; border-bottom: 1px solid var(--border); padding-bottom: 15px; }
label { display: block; margin-bottom: 8px; font-size: 0.9em; color: #cbd5e1; }
input[type="range"] {
width: 100%;
background: var(--bg);
height: 6px;
border-radius: 3px;
appearance: none;
cursor: pointer;
}
input[type="range"]::-webkit-slider-thumb {
appearance: none;
width: 18px;
height: 18px;
background: var(--accent);
border-radius: 50%;
cursor: pointer;
}
#convBlur::-webkit-slider-thumb { background: var(--conv); }
#convSharpen::-webkit-slider-thumb { background: var(--conv); }
.val-display { float: right; color: var(--accent); font-family: monospace; }
.val-conv { float: right; color: var(--conv); font-family: monospace; }
/* Buttons & Selects */
.btn-grid { display: grid; grid-template-columns: 1fr 1fr; gap: 8px; margin-bottom: 8px; }
button, select {
width: 100%;
padding: 10px;
background: var(--panel);
color: var(--text);
border: 1px solid var(--border);
border-radius: 6px;
cursor: pointer;
transition: all 0.2s;
font-size: 0.9em;
font-family: inherit;
}
button:hover, select:hover { background: #334155; }
button.active { background: var(--accent); color: var(--bg); border-color: var(--accent); font-weight: bold;}
select { background-color: #0f172a; margin-bottom: 10px; }
input[type="file"] { display: none; }
.upload-btn { background: #475569; }
/* Visualization Area */
.viz-area {
display: flex;
flex-direction: column;
gap: 20px;
}
.canvas-grid {
display: grid;
grid-template-columns: repeat(2, auto);
gap: 20px;
justify-content: center;
}
.canvas-container {
background: #000;
border: 2px solid var(--border);
border-radius: 8px;
overflow: hidden;
position: relative;
display: flex;
flex-direction: column;
align-items: center;
}
.canvas-label {
background: var(--panel);
width: 100%;
text-align: center;
padding: 8px 0;
font-size: 0.85em;
border-bottom: 1px solid var(--border);
color: #94a3b8;
font-weight: 600;
}
canvas { image-rendering: pixelated; }
/* Inspector */
.inspector {
background: rgba(0,0,0,0.3);
border-radius: 8px;
padding: 15px;
display: grid;
grid-template-columns: auto auto 1fr;
gap: 25px;
align-items: center;
}
.mini-view {
width: 90px;
height: 90px;
border: 2px solid var(--border);
background: #000;
margin-top: 5px;
display: block;
}
.legend-container {
width: 100%;
padding: 5px 15px;
box-sizing: border-box;
background: var(--panel);
border-top: 1px solid var(--border);
}
.legend-bar {
height: 10px;
width: 100%;
background: linear-gradient(to right, #0000ff, #0080ff, #00ffff, #00ff00, #ffff00, #ff0000);
border-radius: 6px;
margin-bottom: 4px;
}
.legend-labels { display: flex; justify-content: space-between; font-size: 0.75em; color: #94a3b8; }
.status-badge {
display: inline-block; padding: 4px 8px; border-radius: 4px;
font-size: 0.8em; font-weight: bold; width: 100%; text-align: center;
box-sizing: border-box; margin-top: 5px;
}
.status-idle { background: #064e3b; color: #34d399; }
.status-busy { background: #78350f; color: #fbbf24; }
.warning-text { font-size: 0.8em; color: var(--danger); margin-top: 5px; display: none; }
#overlayCanvas { position: absolute; top: 33px; left: 0; cursor: crosshair; }
</style>
</head>
<body>
<div style="text-align:center; max-width: 800px; margin-bottom: 20px;">
<h1 style="margin-bottom:5px">Нелокальне усереднення (NLM) для денойзінга</h1>
</div>
<details class="theory-spoiler">
<summary>📚 Теоретична довідка: Non-Local Means (Натисніть, щоб розгорнути)</summary>
<div class="theory-content">
<div class="theory-grid">
<div>
<h4>💡 Основна Ідея</h4>
<p>На відміну від локальних фільтрів (які усереднюють лише фізичних сусідів), <b>Non-Local Means (NLM)</b> використовує <i>надлишковість</i> інформації у зображенні. Будь-яка текстура або край має "двійників" в інших частинах зображення.</p>
<p><b>Алгоритм:</b> Щоб очистити піксель $p$, ми шукаємо схожі на його окіл (патч) інші патчі $q$ в межах зони пошуку і обчислюємо зважене середнє.</p>
<h4>📐 Математична Модель</h4>
<p>Значення відновленого пікселя $NL(p)$:</p>
$$ NL(p) = \frac{1}{Z(p)} \sum_{q \in \Omega} w(p,q) \cdot v(q) $$
<p>Де вага $w(p,q)$ залежить від схожості патчів $P_p$ та $P_q$:</p>
$$ w(p,q) = e^{-\frac{||P_p - P_q||^2_2}{h^2}} $$
<ul>
<li>$||P_p - P_q||^2_2$ — евклідова відстань між кольорами пікселів у патчах.</li>
<li>$h$ — параметр згладжування (сила фільтрації).</li>
<li>$Z(p)$ — нормалізаційний коефіцієнт (сума всіх ваг).</li>
</ul>
</div>
<div>
<h4>🔑 Термінологія</h4>
<ul>
<li><b>Патч (Patch):</b> Квадратна область (наприклад, $5 \times 5$), що оточує піксель. Схожість перевіряється саме за вмістом патчів, а не окремих пікселів.</li>
<li><b>Зона пошуку (Search Window):</b> Область навколо пікселя, де ми шукаємо "двійників".</li>
<li><b>Чебишевська метрика ($L_1$ vs $L_\infty$):</b> У комп'ютерному зорі "радіус $R$" часто означає квадрат зі стороною $2R+1$ (метрика Чебишова), що ми і використовуємо в симуляторі.</li>
<li><b>Self-similarity:</b> Властивість зображення містити повторювані структури.</li>
</ul>
<h4>🚀 Застосування</h4>
<ul>
<li><b>Медична візуалізація (MRI/CT):</b> Видалення шуму Райса без розмиття дрібних деталей тканин.</li>
<li><b>Астрофотографія:</b> Підвищення співвідношення сигнал/шум.</li>
<li><b>Препроцесинг:</b> Покращення якості перед стисненням або розпізнаванням образів.</li>
</ul>
</div>
</div>
</div>
</details>
<div class="container">
<div class="panel">
<h3>1. Вхідні дані</h3>
<div class="control-group">
<label>Роздільна здатність:</label>
<select id="resSelect" onchange="changeResolution()">
<option value="128">128 x 128 (Середнє)</option>
<option value="64">64 x 64 (Швидко)</option>
<option value="256">256 x 256 (Повільно!)</option>
</select>
<div id="resWarning" class="warning-text">⚠️ Увага: 256x256 вимагає значних обчислень. Браузер може підвисати.</div>
<div class="btn-grid">
<button onclick="setPreset('shapes', this)">Геометрія</button>
<button onclick="setPreset('texture', this)">Текстура</button>
<button class="active" onclick="setPreset('mri', this)">МРТ</button>
<button class="upload-btn" onclick="document.getElementById('fileInput').click()">Завантажити</button>
</div>
<input type="file" id="fileInput" accept="image/*" onchange="handleFileUpload(this)">
<div style="margin-top:15px;">
<label>Шум ($\sigma$): <span id="val-noise" class="val-display">26</span></label>
<input type="range" id="noise" min="0" max="80" value="26">
</div>
</div>
<h3>2. Налаштування NLM</h3>
<div style="margin-bottom: 10px;">
<label>Форма області:</label>
<select id="metricSelect" onchange="updateParamsFromDOM()">
<option value="euclidean">Коло (Евклід)</option>
<option value="chebyshev">Квадрат (Чебишев)</option>
</select>
</div>
<div class="control-group">
<label>Радіус Патча ($r$): <span id="val-patch" class="val-display">1 px</span></label>
<input type="range" id="patchSize" min="1" max="5" step="1" value="1">
<div style="font-size:0.75em; color:#64748b; margin-top:-5px; margin-bottom: 10px">Вікно: <span id="lbl-patch-dim" style="color:var(--highlight)">5x5</span></div>
<label>Радіус Пошуку ($R$): <span id="val-search" class="val-display">10 px</span></label>
<input type="range" id="searchRadius" min="5" max="25" step="1" value="10">
<label>Фільтрація ($h$): <span id="val-h" class="val-display">0.7</span></label>
<input type="range" id="paramH" min="0.1" max="10" step="0.1" value="0.7">
<div id="status" class="status-badge status-idle">NLM Готово</div>
</div>
<h3 style="color:var(--conv)">3. Порівняння (Згортковий фільтр)</h3>
<div class="control-group">
<label>Blur (Розмиття): <span id="val-blur" class="val-conv">1</span></label>
<input type="range" id="convBlur" min="0" max="5" step="1" value="1">
<label>Sharpen (Різкість): <span id="val-sharpen" class="val-conv">1</span></label>
<input type="range" id="convSharpen" min="0" max="10" step="1" value="1">
</div>
</div>
<div class="viz-area">
<div class="canvas-grid">
<div class="canvas-container">
<div class="canvas-label">A. Вхідне + Шум (ЛКМ - зафіксувати область)</div>
<canvas id="noisyCanvas" width="256" height="256"></canvas>
<canvas id="overlayCanvas" width="256" height="256"></canvas>
</div>
<div class="canvas-container">
<div class="canvas-label">B. Карта Схожесті NLM</div>
<canvas id="weightCanvas" width="256" height="256"></canvas>
<div class="legend-container">
<div class="legend-bar"></div>
<div class="legend-labels">
<span>0 (Різне)</span>
<span>Вага</span>
<span>1 (Схоже)</span>
</div>
</div>
</div>
<div class="canvas-container" style="border-color: var(--accent);">
<div class="canvas-label" style="color:var(--accent)">C. Результат NLM</div>
<canvas id="outputCanvas" width="256" height="256"></canvas>
</div>
<div class="canvas-container" style="border-color: var(--conv);">
<div class="canvas-label" style="color:var(--conv)">D. Звичайний фільтр</div>
<canvas id="convCanvas" width="256" height="256"></canvas>
</div>
</div>
<div class="panel inspector">
<div style="text-align:center">
<label style="margin-bottom:5px; color:var(--highlight)">Цільовий Патч</label>
<canvas id="patchRef" class="mini-view" width="64" height="64"></canvas>
</div>
<div style="text-align:center">
<label style="margin-bottom:5px">NLM Відновлення</label>
<canvas id="patchAvg" class="mini-view" width="64" height="64"></canvas>
</div>
<div>
<h3 style="margin-top:0; color:#fff">піксель</h3>
<div id="inspectorText" style="font-family: monospace; color: #cbd5e1; font-size: 0.95em; line-height: 1.6;">
Наведіть на зображення А...
</div>
</div>
</div>
</div>
</div>
<script>
// --- Constants & Configuration ---
// Dynamic Resolution Variables
// Dynamic Resolution Variables
let SIZE = 128; // Було 64, ставимо 128
const CANVAS_DISPLAY = 256;
let SCALE = 2; // Було 4. (Розрахунок: 256 / 128 = 2)
// Data Arrays
let originalData = null;
let noisyData = null;
let denoisedData = null;
// Params
let p_sigma = 20;
let p_patch = 2;
let p_search = 10;
let p_h = 0.7;
let p_blur = 1;
let p_sharpen = 1;
let p_metric = 'euclidean';
// UI State
let lastMouseX = -1;
let lastMouseY = -1;
let isFrozen = false;
let debounceTimer;
let currentPresetType = 'shapes'; // Track current preset to reload on res change
// --- Initialization ---
window.addEventListener('load', () => {
initBuffers();
init();
});
function initBuffers() {
originalData = new Float32Array(SIZE * SIZE);
noisyData = new Float32Array(SIZE * SIZE);
denoisedData = new Float32Array(SIZE * SIZE);
// Default center mouse
lastMouseX = Math.floor(SIZE/2);
lastMouseY = Math.floor(SIZE/2);
}
function init() {
// 1. Вмикаємо слайдери та мишку (ЦЬОГО РЯДКА НЕ ВИСТАЧАЛО)
setupControls();
// 2. Знаходимо кнопку MRI (вона третя за рахунком, індекс 2)
const mriBtn = document.querySelectorAll('.btn-grid button')[2];
// 3. Встановлюємо пресет MRI
setPreset('mri', mriBtn);
}
function setupControls() {
// Noise Slider
document.getElementById('noise').addEventListener('input', (e) => {
p_sigma = parseFloat(e.target.value);
document.getElementById('val-noise').innerText = p_sigma;
regenerateNoise();
});
// NLM Params
['patchSize', 'searchRadius', 'paramH'].forEach(id => {
document.getElementById(id).addEventListener('input', updateParamsFromDOM);
});
// Convolution Params
document.getElementById('convBlur').addEventListener('input', (e) => {
p_blur = parseInt(e.target.value);
document.getElementById('val-blur').innerText = p_blur;
runStandardFilter();
});
document.getElementById('convSharpen').addEventListener('input', (e) => {
p_sharpen = parseInt(e.target.value);
document.getElementById('val-sharpen').innerText = p_sharpen;
runStandardFilter();
});
// Mouse Interaction
const overlay = document.getElementById('overlayCanvas');
overlay.addEventListener('mousemove', (e) => {
if (isFrozen) return;
const rect = overlay.getBoundingClientRect();
const mx = Math.floor((e.clientX - rect.left) / SCALE);
const my = Math.floor((e.clientY - rect.top) / SCALE);
if (mx >= 0 && mx < SIZE && my >= 0 && my < SIZE) {
lastMouseX = mx; lastMouseY = my;
visualizeLive();
}
});
overlay.addEventListener('click', () => {
isFrozen = !isFrozen;
const txt = document.getElementById('inspectorText');
txt.style.borderLeft = isFrozen ? "3px solid #f43f5e" : "none";
});
}
function changeResolution() {
const sel = document.getElementById('resSelect');
SIZE = parseInt(sel.value);
SCALE = CANVAS_DISPLAY / SIZE;
// Warning for 256
const warning = document.getElementById('resWarning');
warning.style.display = (SIZE === 256) ? 'block' : 'none';
initBuffers();
// Reload current content
if (currentPresetType === 'file' || currentPresetType === 'mri') {
// If it was a file, we might need to re-crop/re-load.
// For simplicity, let's revert to procedural if it was file,
// OR try to reload if element exists.
// Re-triggering the click is safest if we want to keep flow.
const activeBtn = document.querySelector('.btn-grid button.active');
if (activeBtn.innerText.includes('MRI')) setPreset('mri', activeBtn);
else setPreset('shapes', document.querySelectorAll('.btn-grid button')[0]);
} else {
setPreset(currentPresetType);
}
}
function updateParamsFromDOM() {
p_patch = parseInt(document.getElementById('patchSize').value);
p_search = parseInt(document.getElementById('searchRadius').value);
p_h = parseFloat(document.getElementById('paramH').value);
p_metric = document.getElementById('metricSelect').value;
document.getElementById('val-patch').innerText = p_patch + " px";
document.getElementById('lbl-patch-dim').innerText = `${2*p_patch+1}x${2*p_patch+1}`;
document.getElementById('val-search').innerText = p_search + " px";
document.getElementById('val-h').innerText = p_h.toFixed(1);
visualizeLive();
scheduleFullDenoise();
}
// --- Image Loading Logic ---
function setPreset(type, btn) {
currentPresetType = type;
if(btn) setActiveBtn(btn);
if (type === 'mri') {
const mriBase64 = "data:image/png;base64,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";
loadImageFromFile(mriBase64);
return;
}
// Procedural Generation
generateProcedural(type);
}
function setActiveBtn(btn) {
document.querySelectorAll('.btn-grid button').forEach(b => b.classList.remove('active'));
if(btn) btn.classList.add('active');
}
function handleFileUpload(input) {
if (input.files && input.files[0]) {
const reader = new FileReader();
reader.onload = function(e) {
currentPresetType = 'file';
loadImageFromFile(e.target.result);
setActiveBtn(document.querySelector('.upload-btn'));
}
reader.readAsDataURL(input.files[0]);
}
}
function loadImageFromFile(src) {
const img = new Image();
img.onload = () => {
// CROP Logic
const tempCv = document.createElement('canvas');
tempCv.width = SIZE; tempCv.height = SIZE;
const tempCtx = tempCv.getContext('2d');
// Calculate crop (cover/center)
const minDim = Math.min(img.width, img.height);
const sx = (img.width - minDim) / 2;
const sy = (img.height - minDim) / 2;
// Draw clipped portion to fit SIZE x SIZE
tempCtx.drawImage(img, sx, sy, minDim, minDim, 0, 0, SIZE, SIZE);
const pData = tempCtx.getImageData(0, 0, SIZE, SIZE).data;
// Grayscale conversion
for(let i=0; i<SIZE*SIZE; i++) {
const r = pData[i*4];
const g = pData[i*4+1];
const b = pData[i*4+2];
originalData[i] = 0.299*r + 0.587*g + 0.114*b;
}
regenerateNoise();
};
img.onerror = () => {
alert("Не вдалося завантажити зображення.");
}
img.src = src;
}
function generateProcedural(type) {
// Scale factor for shapes to look consistent across resolutions
const ratio = SIZE / 64;
for (let y = 0; y < SIZE; y++) {
for (let x = 0; x < SIZE; x++) {
let val = 0;
if (type === 'shapes') {
// Scale coordinates
if (x > 15*ratio && x < 45*ratio && y > 15*ratio && y < 45*ratio) val = 200; else val = 50;
if ((x-48*ratio)**2 + (y-48*ratio)**2 < 60*(ratio**2)) val = 150;
} else if (type === 'texture') {
// Texture frequency remains constant or scales?
// Let's keep frequency constant (more texture on larger canvas)
val = ((x + y) % 8 < 4) ? 180 : 40;
}
originalData[y * SIZE + x] = val;
}
}
regenerateNoise();
}
function regenerateNoise() {
const ctx = document.getElementById('noisyCanvas').getContext('2d');
for (let i = 0; i < SIZE * SIZE; i++) {
let noise = (Math.random() + Math.random() + Math.random() + Math.random() - 2) * p_sigma;
noisyData[i] = Math.max(0, Math.min(255, originalData[i] + noise));
}
drawDataToCanvas(ctx, noisyData);
scheduleFullDenoise();
runStandardFilter();
visualizeLive();
}
// --- Standard Filter Logic ---
function runStandardFilter() {
const cvs = document.getElementById('convCanvas');
const ctx = cvs.getContext('2d');
let tempData = new Float32Array(noisyData);
// Blur
if (p_blur > 0) {
const blurRes = new Float32Array(SIZE * SIZE);
const r = p_blur;
for(let y=0; y<SIZE; y++) {
for(let x=0; x<SIZE; x++) {
let sum = 0, count = 0;
for(let dy=-r; dy<=r; dy++) {
for(let dx=-r; dx<=r; dx++) {
let nx = x+dx, ny = y+dy;
if(nx>=0 && nx<SIZE && ny>=0 && ny<SIZE) {
sum += tempData[ny*SIZE+nx];
count++;
}
}
}
blurRes[y*SIZE+x] = sum/count;
}
}
tempData = blurRes;
}
// Sharpen
if (p_sharpen > 0) {
const sharpRes = new Float32Array(SIZE * SIZE);
const s = p_sharpen * 0.5;
for(let y=0; y<SIZE; y++) {
for(let x=0; x<SIZE; x++) {
let center = tempData[y*SIZE+x];
let neighbors = 0;
const dirs = [[0,1], [0,-1], [1,0], [-1,0]];
dirs.forEach(d => {
let nx = x+d[0], ny = y+d[1];
if(nx>=0 && nx<SIZE && ny>=0 && ny<SIZE) neighbors += tempData[ny*SIZE+nx];
else neighbors += center;
});
let val = (1 + 4*s) * center - s * neighbors;
sharpRes[y*SIZE+x] = Math.max(0, Math.min(255, val));
}
}
tempData = sharpRes;
}
drawDataToCanvas(ctx, tempData);
}
// --- NLM Core & Viz ---
function visualizeLive() {
if(lastMouseX < 0) return;
const px = lastMouseX, py = lastMouseY;
const ctxOverlay = document.getElementById('overlayCanvas').getContext('2d');
const ctxWeights = document.getElementById('weightCanvas').getContext('2d');
ctxOverlay.clearRect(0, 0, CANVAS_DISPLAY, CANVAS_DISPLAY);
// Search Box
// Search Box
const sRad = p_search;
// 1. Спочатку обчислюємо межі (це критично для роботи 2-ї канви - карти схожесті)
const sMinX = Math.max(0, px - sRad), sMaxX = Math.min(SIZE-1, px + sRad);
const sMinY = Math.max(0, py - sRad), sMaxY = Math.min(SIZE-1, py + sRad);
ctxOverlay.strokeStyle = 'rgba(56, 255, 255, 0.9)';
ctxOverlay.lineWidth = 2;
ctxOverlay.setLineDash([4, 4]);
// 2. Тепер перевіряємо метрику для візуалізації на 1-й канві
if (p_metric === 'euclidean') {
// Малюємо КОЛО для Евкліда
ctxOverlay.beginPath();
ctxOverlay.arc((px + 0.5) * SCALE, (py + 0.5) * SCALE, sRad * SCALE, 0, Math.PI * 2);
ctxOverlay.stroke();
} else {
// Малюємо КВАДРАТ для Чебишева (або якщо метрика не обрана)
ctxOverlay.strokeRect(sMinX * SCALE, sMinY * SCALE, (sMaxX - sMinX + 1) * SCALE, (sMaxY - sMinY + 1) * SCALE);
}
ctxOverlay.setLineDash([]);
// Patch Box
const pRad = p_patch;
ctxOverlay.strokeStyle = '#facc15'; ctxOverlay.lineWidth = 2;
const pSize = (pRad*2+1);
ctxOverlay.strokeRect((px - pRad) * SCALE, (py - pRad) * SCALE, pSize * SCALE, pSize * SCALE);
// Weights Heatmap
ctxWeights.fillStyle = '#000020'; ctxWeights.fillRect(0,0,CANVAS_DISPLAY, CANVAS_DISPLAY);
let totalWeight = 0, weightedSum = 0, maxW = 0;
const tempWeights = [];
// Scaled H parameter to make filtering aggressiveness independent of noise level
const safeSigma = Math.max(p_sigma, 1.0);
const h2 = (p_h * safeSigma) ** 2;
for (let y = sMinY; y <= sMaxY; y++) {
for (let x = sMinX; x <= sMaxX; x++) {
// Тут (px, py) - центр, а (x, y) - поточний піксель вікна
if (p_metric === 'euclidean') {
const dx = x - px;
const dy = y - py;
if (dx*dx + dy*dy > p_search*p_search) continue;
}
const dist2 = calculatePatchDistance(px, py, x, y);
// IMPROVED WEIGHT FORMULA:
// 1. Removes the hard subtraction "dist2 - 2*sigma^2" which caused excessive blurring ("soaping") at high noise.
// 2. Scales h with sigma so the slider behavior is consistent across different noise levels.
const w = Math.exp( -dist2 / h2 );
if (w > maxW) maxW = w;
totalWeight += w; weightedSum += w * noisyData[y * SIZE + x];
// Add ALL pixels in range to visualization to avoid black holes in heatmap
tempWeights.push({x, y, w});
}
}
if (maxW < 1e-6) maxW = 1;
for (let item of tempWeights) {
const val = item.w / maxW;
const hue = (1.0 - val) * 240;
ctxWeights.fillStyle = `hsla(${hue}, 100%, 50%, 1.0)`;
//ctxWeights.fillStyle = `hsl(${(1 - w) * 240}, 100%, 50%)`; //fне працює
ctxWeights.fillRect(item.x * SCALE, item.y * SCALE, SCALE, SCALE);
}
const resultVal = totalWeight > 0 ? weightedSum / totalWeight : noisyData[py*SIZE+px];
document.getElementById('inspectorText').innerHTML =
`Коорд: <b>(${px}, ${py})</b> ${isFrozen ? '<span style="color:#f43f5e">[FIXED]</span>' : ''}<br>` +
`NLM: <span style="color:#facc15">${resultVal.toFixed(1)}</span> (Orig: ${noisyData[py*SIZE+px].toFixed(0)})<br>` +
``;
// Перший патч показує оригінальний шум (noisyData за замовчуванням)
drawPatchView('patchRef', px, py);
// Другий патч тепер бере дані з denoisedData
// resultVal залишаємо для підсвітки центрального пікселя, який ми щойно розрахували "на льоту"
drawPatchView('patchAvg', px, py, resultVal, denoisedData);
}
function scheduleFullDenoise() {
const statusEl = document.getElementById('status');
statusEl.innerText = "Обробка..."; statusEl.className = "status-badge status-busy";
clearTimeout(debounceTimer);
// Longer debounce for larger images
const delay = (SIZE > 100) ? 200 : 50;
debounceTimer = setTimeout(runFullDenoiseProcess, delay);
}
function runFullDenoiseProcess() {
const start = performance.now();
// Consistent H scaling
const safeSigma = Math.max(p_sigma, 1.0);
const h2 = (p_h * safeSigma) ** 2;
for (let y = 0; y < SIZE; y++) {
for (let x = 0; x < SIZE; x++) {
let totalWeight = 0, weightedSum = 0;
const sMinX = Math.max(0, x - p_search), sMaxX = Math.min(SIZE-1, x + p_search);
const sMinY = Math.max(0, y - p_search), sMaxY = Math.min(SIZE-1, y + p_search);
for (let sy = sMinY; sy <= sMaxY; sy++) {
for (let sx = sMinX; sx <= sMaxX; sx++) {
// Перевіряємо відстань від центру (x, y) до поточного пікселя (sx, sy)
if (p_metric === 'euclidean') {
const dx = sx - x;
const dy = sy - y;
if (dx*dx + dy*dy > p_search*p_search) continue;
}
const dist2 = calculatePatchDistance(x, y, sx, sy);
const w = Math.exp( -dist2 / h2 );
totalWeight += w; weightedSum += w * noisyData[sy * SIZE + sx];
}
}
denoisedData[y*SIZE+x] = totalWeight > 0 ? weightedSum/totalWeight : noisyData[y*SIZE+x];
}
}
drawDataToCanvas(document.getElementById('outputCanvas').getContext('2d'), denoisedData);
const statusEl = document.getElementById('status');
statusEl.innerText = `NLM Готово (${(performance.now() - start).toFixed(0)} ms)`;
statusEl.className = "status-badge status-idle";
}
// --- Helpers ---
function calculatePatchDistance(x1, y1, x2, y2) {
let dist = 0; const dim = p_patch;
for (let dy = -dim; dy <= dim; dy++) {
for (let dx = -dim; dx <= dim; dx++) {
const val1 = getSafePixel(x1 + dx, y1 + dy);
const val2 = getSafePixel(x2 + dx, y2 + dy);
dist += (val1 - val2) ** 2;
}
}
return dist / ((2*dim+1)**2);
}
function getSafePixel(x, y, sourceData = noisyData) {
if (x < 0) x = -x; if (y < 0) y = -y;
if (x >= SIZE) x = 2*SIZE - x - 2; if (y >= SIZE) y = 2*SIZE - y - 2;
if (x < 0) x = 0; if (x >= SIZE) x = SIZE-1;
if (y < 0) y = 0; if (y >= SIZE) y = SIZE-1;
// Використовуємо переданий масив або noisyData за замовчуванням
return sourceData[y * SIZE + x];
}
function drawDataToCanvas(ctx, data) {
ctx.fillStyle = '#000'; ctx.fillRect(0,0,CANVAS_DISPLAY, CANVAS_DISPLAY);
for (let y = 0; y < SIZE; y++) {
for (let x = 0; x < SIZE; x++) {
const val = Math.floor(data[y * SIZE + x]);
ctx.fillStyle = `rgb(${val},${val},${val})`;
ctx.fillRect(x*SCALE, y*SCALE, SCALE, SCALE);
}
}
}
function drawPatchView(canvasId, cx, cy, overrideCenter = null, dataSrc = noisyData) {
const cvs = document.getElementById(canvasId);
const ctx = cvs.getContext('2d');
const w = cvs.width;
const dim = p_patch * 2 + 1;
const blockSize = w / dim;
ctx.fillStyle = '#000'; ctx.fillRect(0,0,w,w);
for (let dy = 0; dy < dim; dy++) {
for (let dx = 0; dx < dim; dx++) {
const lx = cx + (dx - p_patch);
const ly = cy + (dy - p_patch);
// ТУТ ЗМІНА: Передаємо dataSrc у getSafePixel
let val = getSafePixel(lx, ly, dataSrc);
const isCenter = (dx === p_patch && dy === p_patch);
if (overrideCenter !== null && isCenter) val = overrideCenter;
val = Math.floor(val);
ctx.fillStyle = `rgb(${val},${val},${val})`;
const drawX = Math.floor(dx * blockSize);
const drawY = Math.floor(dy * blockSize);
const drawSize = Math.ceil(blockSize);
ctx.fillRect(drawX, drawY, drawSize, drawSize);
if (isCenter) {
ctx.strokeStyle = overrideCenter !== null ? '#10b981' : '#facc15';
ctx.lineWidth = 2;
ctx.strokeRect(drawX+1, drawY+1, drawSize-2, drawSize-2);
}
}
}
}
</script>
</body>
</html>