In this paper we present a novel method to record high spatial resolution far-infrared(FIR) hologram. This method takes advantage of the photo-induced phase transition characteristic of vanadium dioxide(VO2) film. The light path is off-axis digital hologram recording path, while the VO2 film is kept in constant temperature in front of the recording high resolution CMOS sensor. In the setup, the far infrared light from CO2 laser changes the partial transmittance of VO2 film to visible light, then a read-out visible laser is used to measure the transmittance of VO2 film, and subsequently the results are recorded by a high resolution CMOS sensor. So that with utilizing the photo-induced phase transition of VO2 film, we can use CMOS sensor to record far-infrared digital hologram. As the pixel pitch of CMOS sensor is much smaller than tradition FIR sensor, the recorded FIR digital hologram has been much improved. Moreover, the transition speed of VO2 film is in nanosecond scale which means that far-infrared fast-moving object recording and hologram video could be achieved. In our experiments we used different objects to compare the spatial recording resolution and the experiments prove that our method can record higher spatial spatial resolution than traditional FIR digital hologram. It has the potential to become a more effective FIR digital hologram record method. Further research will focus on the simplified light path and FIR hologram video record and process.
Restricted by the detector technology and optical diffraction limit, the spatial resolution of infrared imaging system is
difficult to achieve significant improvement. Super-Resolution (SR) reconstruction algorithm is an effective way to solve
this problem. Among them, the SR algorithm based on multichannel blind deconvolution (MBD) estimates the
convolution kernel only by low resolution observation images, according to the appropriate regularization constraints
introduced by a priori assumption, to realize the high resolution image restoration. The algorithm has been shown
effective when each channel is prime. In this paper, we use the significant edges to estimate the convolution kernel and
introduce an adaptive convolution kernel size selection mechanism, according to the uncertainty of the convolution
kernel size in MBD processing. To reduce the interference of noise, we amend the convolution kernel in an iterative
process, and finally restore a clear image. Experimental results show that the algorithm can meet the convergence
requirement of the convolution kernel estimation.