ChristineHitha
Introduction
Images captured under low-light conditions suffer from degradation, such as low visibility, low contrast, and high-level noise. Although they can be alleviated by professional devices and advanced photographic skills to a certain extent, the inherent cause of the noise is unavoidable and cannot be addressed at the hardware level. Without sufficient amount of light, the output of camera sensors is often buried in the intrinsic noise in the system. Longer exposure time can effectively increase the signal-to-noise ratio (SNR) and generate a noise-free image, however it breeds new problems such as motion blur. Thus, low-light image enhancement technique at the software level is highly desired in consumer photography. Moreover, such technique can also benefit many computer vision algorithms (object detection, tracking, etc.) since their performance highly relies on the visibility of the target scene.
Background
Methods
In order to generate a low light image, we utilized ISETCAM which is a matlab toolbox
ISETCAM Simulation
ISETCAM is split into four main modules: scene, optics, sensor, and processing. For each module, there are useful create, set, and get parameters as well as a display (window) function which allows the user to experiment with the simulation and then see the result. In addition, there are extra built-in functions which allow for even more customization. A good start would be in the script t_introduction2ISET.m found in /isetcam/tutorials/introduction. From there, we were able to build our own script to simulate the scene, optics, sensor, and processing pipeline. First, we generated a scene from a jpeg file and then adjusted the mean luminance of the scene in order to simulate low light conditions. From our image which originally had a mean luminance of 35.4 cd/m, we adjusted it to be less than a third of that to 10 cd/m.
I = im2double(imread('img.jpg'));
scene = sceneFromFile(I, 'rgb');
scene = sceneAdjustLuminance(scene, 'mean', 10);
In the figure above, the after illuminance energy has noticeably decreased by a magnitude of 10 due to the nature of the scene being low light. The effects of this will be seen during the sensor stage. There will be lower number of photons hitting the sensor which would yield a low light image as well as increase the shot noise of the captured image compared to a well lit scene. The problem with this noise will be seen and addressed later on.
Denoising after ISET ISP
Image denoising refers to the recovery of a digital image that has been impure by the noise. In case of image denoising methods, the characteristics of the degrading system and the noises are assumed to be known beforehand.The image s(x,y) is blurred by a linear operation and noise n(x,y) is added to form the degraded image w(x,y). This is convolved with the restoration procedure g(x,y) to produce the restored image z(x,y). The ―Linear operation‖ shown in Fig .2 is the addition or multiplication of the noise n(x,y) to the signal s(x,y).Once the corrupted image w(x,y) is obtained, it is subjected to the denoising technique to get the denoised image z(x,y).The point of focus in this thesis is comparing and contrasting several ―denoising techniques‖ (Fig. 2) [7].
Results
Conclusions
References
A simulation tool for evaluating digital camera image quality (2004). J. E. Farrell, F. Xiao, P. Catrysse, B. Wandell . Proc. SPIE vol. 5294, p. 124-131, Image Quality and System Performance, Miyake and Rasmussen (Eds). January 2004