scholarly journals Noise Reduction Method for Image Signal Processor Based on Unified Image Sensor Noise Model

2013 ◽  
Vol E96.D (5) ◽  
pp. 1152-1161
Author(s):  
Yeul-Min BAEK ◽  
Whoi-Yul KIM
Keyword(s):  
Noise Reduction ◽  
Reduction Method ◽  
Image Sensor ◽  
Noise Model ◽  
Sensor Noise ◽  
Image Signal Processor ◽  
Signal Processor

scholarly journals Image Pre-Processing Method of Machine Learning for Edge Detection with Image Signal Processor Enhancement

Micromachines ◽  
2021 ◽  
Vol 12 (1) ◽  
pp. 73
Author(s):  
Keumsun Park ◽  
Minah Chae ◽  
Jae Hyuk Cho
Keyword(s):  
Machine Learning ◽  
Edge Detection ◽  
Image Sensor ◽  
Processing Method ◽  
Oxide Semiconductor ◽  
Support Vector ◽  
K Nearest Neighbor ◽  
Comparison Results ◽  
Image Signal Processor ◽  
Signal Processor

Even though computer vision has been developing, edge detection is still one of the challenges in that field. It comes from the limitations of the complementary metal oxide semiconductor (CMOS) Image sensor used to collect the image data, and then image signal processor (ISP) is additionally required to understand the information received from each pixel and performs certain processing operations for edge detection. Even with/without ISP, as an output of hardware (camera, ISP), the original image is too raw to proceed edge detection image, because it can include extreme brightness and contrast, which is the key factor of image for edge detection. To reduce the onerousness, we propose a pre-processing method to obtain optimized brightness and contrast for improved edge detection. In the pre-processing, we extract meaningful features from image information and perform machine learning such as k-nearest neighbor (KNN), multilayer perceptron (MLP) and support vector machine (SVM) to obtain enhanced model by adjusting brightness and contrast. The comparison results of F1 score on edgy detection image of non-treated, pre-processed and pre-processed with machine learned are shown. The pre-processed with machine learned F1 result shows an average of 0.822, which is 2.7 times better results than the non-treated one. Eventually, the proposed pre-processing and machine learning method is proved as the essential method of pre-processing image from ISP in order to gain better edge detection image. In addition, if we go through the pre-processing method that we proposed, it is possible to more clearly and easily determine the object required when performing auto white balance (AWB) or auto exposure (AE) in the ISP. It helps to perform faster and more efficiently through the proactive ISP.

Testable design for cell-based design ASICs and its application to VLSI image signal processor

1993 ◽  
Vol 76 (4) ◽  
pp. 47-56
Author(s):  
Hiroyuki Kawai ◽  
Shin-Ichi Nakagawa ◽  
Masahiko Yoshimoto ◽  
Yasutaka Horiba ◽  
Takuji Ogihara ◽  
...  
Keyword(s):  
Image Signal Processor ◽  
Testable Design ◽  
Signal Processor

scholarly journals A parallel camera image signal processor for SIMD architecture

2016 ◽  
Vol 2016 (1) ◽  
Author(s):  
Seung-Hyun Choi ◽  
Junguk Cho ◽  
Yong-Min Tai ◽  
Seong-Won Lee
Keyword(s):  
Camera Image ◽  
Simd Architecture ◽  
Image Signal Processor ◽  
Signal Processor
Sign in / Sign up

Export Citation Format

Share Document