In this paper, a 128x128, 34μm pixel-pitch, room temperature infrared image sensor and processor is presented. With a measured power consumption of 8.9mW (540μW / pixel) in full operating mode (image acquisition and data processing), the sensor exhibit a Noise Equivalent Temperature Difference (NETD) of 190mK at room temperature and doesn't require a Thermo-Electric Cooler (TEC). The circuit also features a novel ∑Δ Analogue to Digital Conversion architecture, 12 frames of Built-in SRAM and 128 column-wise full-custom processors that target a broad range of applications such as 2-points corrected IR camera or feature extraction for privacy-compliant presence detection, localization and counting. Built-in analogue and digital pixel-level offset pre-correction improves operability and manufacturing yields thus pushing bolometers IR technology one step forward towards high-end applications for consumer market.
This paper investigates the compression of infrared images with three codecs: JPEG2000, JPEG-XT and HEVC. Results are evaluated in terms of SNR, Mean Relative Squared Error (MRSE) and the HDR-VDP2 quality metric. JPEG2000 and HEVC perform fairy similar and better than JPEG-XT. JPEG2000 performs best for bits-per-pixel rates below 1.4 bpp, while HEVC obtains best performance in the range 1.4 to 6.5 bpp. The compression performance is also evaluated based on maximum errors. These results also show that HEVC can achieve a precision of 1°C with an average of 1.3 bpp.
In this paper, for infrared images, the image enhancement technique based on wavelet transform is studied, which is a process that automatically apply different filtering coefficient toward different directions. The algorithm, including the application of nonlinear anisotropic diffusion, is experienced to the enhancement of infrared images. For directional filtering, the structural feature at each pixel is analyzed by the eigen-analysis. If the analysis shows that the pixel belongs to the edge region, we then perform directional smoothing along the tangential direction of the edge to improve its continuity, while directional sharpening along the normal direction to enhance the contrast. Meanwhile, the noise in the homogeneous region has been reduced notably by applying the appropriate wavelet coefficient. The algorithm is so effective that it reduces the noise while enhancing the edge sharpness at the same time. The quantitative measurements along with the visual inspection were also compared and results showed the algorithm based on wavelet transform has the ability in enhancing the infrared image. The proposed algorithm is compared to the other regular noise-reducing algorithms. The experimental results show that the proposed algorithm considerably improves the infrared image quality without causing any noticeable artifacts. Out of the algorithms compared, our algorithm demonstrated the best performance.