Jinsong Liu

Chongqing University of Posts and Telecommunications · School of Communication and Information Engineering

Super-resolution (SR) is a widely used technology that increases image resolution using algorithmic methods. However, preserving the local edge structure and visual quality in infrared (IR) SR images is challenging because of their disadvantages, such as lack of detail, poor contrast, and blurry edges. Traditional and advanced methods maintain the...

Aiming at the linear temperature compensation algorithm’s disadvantage of temperature measurement error in rotary kiln infrared scanning temperature measurement process, this paper proposes a precise nonlinear cubic polynomial fitting temperature compensation algorithm. The proposed algorithm compensates the temperature values of scanning points on...

The traditional projection onto convex sets (POCS) super-resolution (SR) reconstruction algorithm can only get reconstructed images with poor contrast, low signal-to-noise ratio and blurring edges. In order to solve the above disadvantages, an improved POCS SR infrared image reconstruction algorithm based on visual mechanism is proposed, which intr...

Aiming at traditional horizontal equal-angle scanning method's disadvantage of measurement error, a high precision equal-angle scanning method is proposed, the proposed method establishes a tilt scanning model by the following steps: introducing height variable, precisely calculating the viewing angle, building scanning model. The model is used to...

Aiming at solving the problem of low resolution and visual blur in infrared imaging, a super-resolution infrared image reconstruction method using human vision processing mechanism (HVPM) was proposed. This method combined a mechanism of vision lateral inhibition with an algorithm projection onto convex sets (POCS) reconstruction, the improved visi...

Aiming at traditional lateral inhibition network enhancement algorithm’s disadvantages such as poor anti-noise performance and complicated calculation, this paper proposed a novel infrared image detail enhancement algorithm based on adaptive lateral inhibition network. The algorithm can not only reduce noise by adaptively changing lateral inhibitio...

For conventional reconstruction algorithms, regularization parameter is randomly selected and image reconstruction cannot achieve the desired display effect. Thus this paper presents a simple and efficient adaptive regularization technique of infrared image super-resolution reconstruction algorithm that combines L1-norm with the total variation reg...

Aiming at infrared images' disadvantages such as low contrast and blur edges, an infrared image enhancement algorithm using lateral inhibition of human visual system (HVS) is proposed. The algorithm makes use of the rapid decline properties of exponential function to reconstruct lateral inhibition coefficient distribution model based on exponential...

In order to avoid these disadvantages like poor contrast, low signal-to-noise ratio, and blurry visual effect of infrared images reconstructed using the existing super-resolution image reconstruction methods, an improved reconstruction algorithm of infrared images is proposed based on human visual system. The algorithm introduces the contrast as pr...

Aiming at these disadvantages like lack of details, poor contrast and blurry edges of infrared images reconstructed by traditional controllable microscanning super-resolution reconstruction (SRR), this paper proposes a novel algorithm, which samples multiple low-resolution images (LRIs) by uncontrollable microscanning, and then uses LRIs as chromos...

The gray level transformation combining with local standard deviation is an effective and efficient way to enhance the contrast of gray images without much calculating burden. Conventional algorithms always calculate pixel's local standard deviation in a rectangular area centered to itself, which fail to take the natural traits of the image's conte...