Image Signal

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The Experts below are selected from a list of 360 Experts worldwide ranked by ideXlab platform

Mahesh H B - One of the best experts on this subject based on the ideXlab platform.

  • least square channel estimation for Image transmission with ofdm over fading channel
    arXiv: Signal Processing, 2020
    Co-Authors: Usha S M, Mahesh H B
    Abstract:

    Wireless communication is the most effective communication to convey audio or video information among the population. It enables the masses to connect throughout the world. Wireless technologies improve the lifestyle of individuals in rural and poor communication areas. In this view, the quality of a reliable Signal can be enhanced by minimizing carrier interference. In this paper bit error rate of an Image, Signal is transmitted over fading channel is analyzed using orthogonal frequency multiplexing and channel estimation technique. An Orthogonal Frequency Multiplexing (OFDM) provides prominent bandwidth effectiveness and improved immunity to the fading environments. In OFDM, the data is modulated using multiple numbers of subcarriers that are orthogonal to each other. A cyclic prefix is infixed between OFDM symbols to annihilate the inter symbol interference (ISI) and inter-carrier interference (ICI). The least square channel estimation method is used to minimize the effect of multipath fading. An Image, Signal is modulated using BPSK, QPSK, 16QAM and 64QAM digital modulation schemes with OFDM and channel estimation and transmitted over AWGN and fading channel. The objective of this work is to improve the Signal to noise ratio by reducing interference.

Xuesong Gao - One of the best experts on this subject based on the ideXlab platform.

  • llisp low light Image Signal processing net via two stage network
    IEEE Access, 2021
    Co-Authors: Hongjin Zhu, Yang Zhao, Rongjie Wang, Ronggang Wang, Weiqiang Chen, Xuesong Gao
    Abstract:

    Images taken in extremely low light suffer from various problems such as heavy noise, blur, and color distortion. Assuming the low-light Images contain a good representation of the scene content, current enhancement methods focus on finding a suitable illumination adjustment but often fail to deal with heavy noise and color distortion. Recently, some works try to suppress noise and reconstruct low-light Images from raw data. But these works apply a network instead of an Image Signal processing pipeline (ISP) to map the raw data to enhanced results which leads to heavy learning burden for the network and get unsatisfactory results. In order to remove heavy noise, correct color bias and enhance details more effectively, we propose a two-stage Low Light Image Signal Processing Network named LLISP. The design of our network is inspired by the traditional ISP: processing the Images in multiple stages according to the attributes of different tasks. In the first stage, a simple denoising module is introduced to reduce heavy noise. In the second stage, we propose a two-branch network to reconstruct the low-light Images and enhance texture details. One branch aims at correcting color distortion and restoring Image content, while another branch focuses on recovering realistic texture. Experimental results demonstrate that the proposed method can reconstruct high-quality Images from low-light raw data and replace the traditional ISP.

Usha S M - One of the best experts on this subject based on the ideXlab platform.

  • least square channel estimation for Image transmission with ofdm over fading channel
    arXiv: Signal Processing, 2020
    Co-Authors: Usha S M, Mahesh H B
    Abstract:

    Wireless communication is the most effective communication to convey audio or video information among the population. It enables the masses to connect throughout the world. Wireless technologies improve the lifestyle of individuals in rural and poor communication areas. In this view, the quality of a reliable Signal can be enhanced by minimizing carrier interference. In this paper bit error rate of an Image, Signal is transmitted over fading channel is analyzed using orthogonal frequency multiplexing and channel estimation technique. An Orthogonal Frequency Multiplexing (OFDM) provides prominent bandwidth effectiveness and improved immunity to the fading environments. In OFDM, the data is modulated using multiple numbers of subcarriers that are orthogonal to each other. A cyclic prefix is infixed between OFDM symbols to annihilate the inter symbol interference (ISI) and inter-carrier interference (ICI). The least square channel estimation method is used to minimize the effect of multipath fading. An Image, Signal is modulated using BPSK, QPSK, 16QAM and 64QAM digital modulation schemes with OFDM and channel estimation and transmitted over AWGN and fading channel. The objective of this work is to improve the Signal to noise ratio by reducing interference.

Hongjin Zhu - One of the best experts on this subject based on the ideXlab platform.

  • llisp low light Image Signal processing net via two stage network
    IEEE Access, 2021
    Co-Authors: Hongjin Zhu, Yang Zhao, Rongjie Wang, Ronggang Wang, Weiqiang Chen, Xuesong Gao
    Abstract:

    Images taken in extremely low light suffer from various problems such as heavy noise, blur, and color distortion. Assuming the low-light Images contain a good representation of the scene content, current enhancement methods focus on finding a suitable illumination adjustment but often fail to deal with heavy noise and color distortion. Recently, some works try to suppress noise and reconstruct low-light Images from raw data. But these works apply a network instead of an Image Signal processing pipeline (ISP) to map the raw data to enhanced results which leads to heavy learning burden for the network and get unsatisfactory results. In order to remove heavy noise, correct color bias and enhance details more effectively, we propose a two-stage Low Light Image Signal Processing Network named LLISP. The design of our network is inspired by the traditional ISP: processing the Images in multiple stages according to the attributes of different tasks. In the first stage, a simple denoising module is introduced to reduce heavy noise. In the second stage, we propose a two-branch network to reconstruct the low-light Images and enhance texture details. One branch aims at correcting color distortion and restoring Image content, while another branch focuses on recovering realistic texture. Experimental results demonstrate that the proposed method can reconstruct high-quality Images from low-light raw data and replace the traditional ISP.

Andreas Moshovos - One of the best experts on this subject based on the ideXlab platform.

  • Image Signal processors on fpgas
    Field-Programmable Custom Computing Machines, 2014
    Co-Authors: Andreas Moshovos
    Abstract:

    An Image Signal Processor (ISP) converts raw imaging sensor data into a format appropriate for further processing and human inspection. This work explores FPGA-based ISP designs considering specialized and programmable implementations and proposes an ISP using a programmable generic processing unit with comparable performance versus the dedicated implementations.