PERBAIKAN CITRA Pertemuan 15 Oleh : Ir. H. Sirait, MT Web/Blog : http://www.hsirait.wordpress.com Phone : 081356633766 FB : Hasanuddin MP Sirait TW : @hsirait BBM : 29C01DD4 Keyword : hsirait

Introduction Apa itu perbaikan citra? Proses peningkatan kualitas visual dari citra karena proses pengambilan gambar yang tidak ideal. (Tidak fokus, gerakan blurring, illumminasi yang jelek, dll) Menilai kualitas visual citra

A Plague in Image Processing: Blur Dari mana blur itu berasal? Optical blur: kamera out-of-focus Motion blur: Kamera bergerak Mengapa perlu deblurring? Mengganggu secara visual Target yang salah untuk kompressi Jelek untuk dianalisa Numerous applications in astronomical imaging, biomedical imaging, biometrics ...

Restoration Images

Another Example

The Real (Optical) Solution Before the repair After the repair

Application (II): Medical Image Deblurring (Deconvolution)

Application (III): Law Enforcement Motion-blurred license plate image

Restoration Example

A Grand Challenge in Iris Recognition out-of-focus iris image

Modeling Blurring Process • Linear degradation model y(m,n) x(m,n) h(m,n) + blurring filter additive white Gaussian noise

The Curse of Noise z(m,n) y(m,n) x(m,n) h(m,n) + Blurring SNR

h(m,n): 1D horizontal motion blurring [1 1 1 1 1 1 1]/7 Image Example x(m,n) BSNR=10dB BSNR=40dB h(m,n): 1D horizontal motion blurring [1 1 1 1 1 1 1]/7

Mengapa butuh metode non linier ? Proses pemodelan degradasi citra dengan linear system muncul terutama karena dapat dinyatakan secara matematis Tetapi ada fenomena dalam visualisasi dan pencitraan secara fisik sulit dijelaskan degna persamaan sederhana secara linier Examples: relationship between illumination and luminance on a complex surface, quantization of intensity values, Gamma-correction in display devices

Point Operations Overview Point operations are zero-memory operations where a given gray level x[0,L] is mapped to another gray level y[0,L] according to a transformation y L x L L=255: for grayscale images

Lazy Man Operation y L x L No influence on visual quality at all

Digital Negative L x L

Contrast Stretching yb ya x a b L

Clipping x a b L

Range Compression x L c=100

Kesimpulan operasi titik Pemetaan f(x) mengahasilkan hasil perbaikan yang berbeda beda Bagaimana memilih fungsi f(x) yang sesuai untuk sembarang citra? One systematic solution is based on the histogram information of an image Histogram equalization and specification

Histogram based Enhancement Histogram of an image represents the relative frequency of occurrence of various gray levels in the image MATLAB function >imhist(x)

Why Histogram? It is a baby in the cradle! Histogram information reveals that image is under-exposed

Another Example Over-exposed image

How to Adjust the Image? Histogram equalization Basic idea: find a map f(x) such that the histogram of the modified (equalized) image is flat (uniform). Key motivation: cumulative probability function (cdf) of a random variable approximates a uniform distribution Suppose h(t) is the histogram (pdf)

Histogram Equalization Uniform Quantization Note: y cumulative probability function L 1 x L

MATLAB Implementation function y=hist_eq(x) [M,N]=size(x); for i=1:256 h(i)=sum(sum(x= =i-1)); End y=x;s=sum(h); I=find(x= =i-1); y(I)=sum(h(1:i))/s*255; end Calculate the histogram of the input image Perform histogram equalization

Ic. Histogram Equalization Histogram: diagram yang menunjukkan jumlah kemunculan grey level (0-255) pada suatu citra Histogram processing: Gambar gelap: histogram cenderung ke sebelah kiri Gambar terang: histogram cenderung ke sebelah kanan Gambar low contrast: histogram mengumpul di suatu tempat Gambar high contrast: histogram merata di semua tempat  Histogram processing: mengubah bentuk histogram agar pemetaan gray level pada citra juga berubah 01/06/2018 PERTEMUAN KE-6

Ic. Histogram Equalization in all grey level and all area (1) Ide: mengubah pemetaan greylevel agar sebarannya (kontrasnya) lebih menyebar pada kisaran 0-255 Sifat: Grey level yang sering muncul lebih dijarangkan jaraknya dengan grey level sebelumnya Grey level yang jarang muncul bisa lebih dirapatkan jaraknya dengan grey level sebelumnya Histogram baru pasti mencapai nilai maksimal keabuan (contoh: 255) 01/06/2018 PERTEMUAN KE-6

Ic. Histogram Equalization in all grey level and all area (2) mengubah pemetaan grey level pada citra, dengan rumus: 01/06/2018 PERTEMUAN KE-6

Ic. Histogram Equalization in all grey level and all area (3) Citra awal: 3 5 5 5 4 5 4 5 4 4 5 3 4 4 4 4 5 6 6 3 Citra Akhir: 1 9 9 9 5 9 5 9 5 5 9 1 5 5 5 5 9 10 10 1 Contoh : citra dengan derajat keabuan hanya berkisar 0-10 Derajat Keabuan Kemunculan Probabilitas Kemunculan Sk SK * 10 Derajat keabuan baru 1 2 3 4 5 6 7 8 9 10 0.15 0.40 0.35 0.1 0.55 0.90 1.5 5.5 01/06/2018 PERTEMUAN KE-6

Ic. Histogram Equalization specific grey level (hist. specification) Histogram equalization tidak dilakukan pada seluruh bagian dari histrogram tapi hanya pada bagian tertentu saja 01/06/2018 PERTEMUAN KE-6

Image Example before after

Histogram Comparison before equalization after equalization

Application (I): Digital Photography

Application (II): Iris Recognition before after

Application (III): Microarray Techniques before after

Frequency-Domain Techniques (I): Unsharp Masking g(m,n) is a high-pass filtered version of x(m,n) • Example (Laplacian operator)

MATLAB Implementation % Implementation of Unsharp masking function y=unsharp_masking(x,lambda) % Laplacian operation h=[0 -1 0;-1 4 -1;0 -1 0]/4; dx=filter2(h,x); y=x+lambda*dx;

1D Example xlp(n) x(n) g(n)=x(n)-xlp(n)

2D Example MATLAB command >roidemo

Frequency-Domain Techniques (II): Homomorphic filtering Basic idea: Illumination (low freq.) reflectance (high freq.) freq. domain enhancement

Image Example before after

Summary of Nonlinear Image Enhancement Understand how image degradation occurs first Play detective: look at histogram distribution, noise statistics, frequency-domain coefficients… Model image degradation mathematically and try inverse-engineering Visual quality is often the simplest way of evaluating the effectiveness, but it will be more desirable to measure the performance at a system level Iris recognition: ROC curve of overall system Microarray: ground-truth of microarray image segmentation result provided by biologists